To prevent incidents and improve efficiency, railcar owners and operators need an understanding of how their assets are performing. With heavier cars in service, there is a greater need to identify “bad actors” (cars which can damage track infrastructure and lead to derailments) as soon as their performance becomes unacceptable. There is also a need to increase average train speed by improving high speed performance and reducing unplanned service interruptions through mechanical failures. Car owners increasingly seek to implement preventative maintenance programs to predict and avoid mechanical failures in the field and to efficiently schedule repairs at a facility and time of their choice. Finally, with more automation of rail operations and increasing regulation to improve safety, the railroad industry needs new ways to monitor the performance of trains, cars and railcar trucks.
Even minor mechanical failures could lead very quickly to a catastrophic failure, not only of a single railcar, but of the entire train. Thus it is desirable to detect and report deviations from operational norms or predictions of impending failure to the locomotive or to a central data handling facility as quickly as possible, allowing for timely human intervention.
One approach in use in North America is the use of wayside defect detectors at fixed locations throughout the railroad network. Detectors measuring bearing temperature (hotbox detectors) are common, while other wayside detectors to measure wheel impacts, bearing condition (from acoustical signatures) and lateral forces are gradually being introduced. However, while one detector can monitor many freight cars as they pass, they can only provide a spot check on performance. It is quite possible that defects will only become apparent and escalate to a critical level between detectors.
Another approach to railcar performance monitoring has been to use on-board instrumentation. One such prominent system has been developed for the Federal Railroad Administration. In this and other similar systems, a number of instruments on different areas of a freight car are used to make discrete measurements before being communicated to a central hub on the freight car. While providing a superior solution to that provided by wayside monitors, wiring, complexity and costs increase the investment required to monitor the cars and decrease efficiency and reliability.
The current systems, however, lack the ability to apply heuristics to act on data gathered from more than one sensor or to detect operational deviations or trends which show deviations from nominal operating parameters. Furthermore, current systems are limited in that they lack the ability to apply such heuristics at multiple levels, for example, at the individual sensor level, at the railcar level, and at the train level. Lastly, current systems lack the ability for sensors to efficiently and reliably communicate their data to a central data gathering facility using a wireless communications infrastructure that has multiple redundancies and which allows communication of data between individual sensors.
Therefore, it would be desirable to have a system which addresses these current deficiencies and which improves (1) the ability to reliably collect and utilize data from multiple sensors on each railcar; (2) the ability to analyze collected data by the application of heuristics to detect and predict operational deficiencies; and (3) the ability to determine the severity of detected conditions to determine if immediate alarms should be raised to facilitate human intervention.